Bioelectronics and Neuroscience, The MARCS Institute, University of Western Sydney Sydney, NSW, Australia.
Front Neurosci. 2014 Mar 18;8:51. doi: 10.3389/fnins.2014.00051. eCollection 2014.
We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.
我们提出了一种可重构的多时钟脉冲神经网络的混合信号实现，该网络能够存储和回忆时空模式。所提出的神经网络包含一个神经元阵列和一个轴突阵列。利用尖峰时间相关延迟可塑性来微调延迟并为网络添加动态。在我们的混合信号实现中，神经元和轴突已被实现为模拟和数字电路。因此，该系统由一个 FPGA 组成，其中包含数字神经元阵列和数字轴突阵列，以及一个模拟 IC，其中包含模拟神经元阵列和模拟轴突阵列。系统可以轻松配置为使用每种组合的不同组合。我们展示并讨论了模拟和数字轴突阵列以及模拟和数字神经元阵列的所有组合的实验结果。测试结果表明，所提出的神经网络能够使用模拟和数字电路成功地回忆起超过 85%的存储模式。
